We adopt pre-trained StyleGANGenForce: https://github.com/genforce/genforce [1]} or StyleGAN2BasicSR: https://github.com/xinntao/BasicSR [2]} (depending on the availability of pre-trained models) as our latent bank, and use the publicly available codes of existing methods for the comparison in this section.
To maintain fairness, we train our model and baselines on the same datasets, including FFHQ [1]} and LSUN [4]}, so that the difference in restoration quality is mainly caused by the algorithms instead of the training distribution. Test set is strictly exclusive from the training. Detailed experimental settings are provided in the appendix.
